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Data Mining introduction and basic concepts

This document provides an introduction to data mining, highlighting its significance due to the explosive growth of data and the necessity for automated analysis. It covers key definitions, functions, methodologies, and categorization of data mining techniques and algorithms, as well as the evolution of data science and database technology. The document also addresses major issues in data mining, such as scalability and effectiveness, and outlines the historical context and development of this field.

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April 18, 2024 Data Mining: Concepts and Techniques 1 Chapter 1. Introduction  Motivation: Why data mining?  What is data mining?  Data Mining: On what kind of data?  Data mining functionality  Classification of data mining systems  Top-10 most popular data mining algorithms  Major issues in data mining  Overview of the course
April 18, 2024 Data Mining: Concepts and Techniques 2 Why Data Mining?  The Explosive Growth of Data: from terabytes to petabytes  Data collection and data availability  Automated data collection tools, database systems, Web, computerized society  Major sources of abundant data  Business: Web, e-commerce, transactions, stocks, …  Science: Remote sensing, bioinformatics, scientific simulation, …  Society and everyone: news, digital cameras, YouTube  We are drowning in data, but starving for knowledge!  “Necessity is the mother of invention”—Data mining—Automated analysis of massive data sets
April 18, 2024 Data Mining: Concepts and Techniques 3 Evolution of Sciences  Before 1600, empirical science  1600-1950s, theoretical science  Each discipline has grown a theoretical component. Theoretical models often motivate experiments and generalize our understanding.  1950s-1990s, computational science  Over the last 50 years, most disciplines have grown a third, computational branch (e.g. empirical, theoretical, and computational ecology, or physics, or linguistics.)  Computational Science traditionally meant simulation. It grew out of our inability to find closed-form solutions for complex mathematical models.  1990-now, data science  The flood of data from new scientific instruments and simulations  The ability to economically store and manage petabytes of data online  The Internet and computing Grid that makes all these archives universally accessible  Scientific info. management, acquisition, organization, query, and visualization tasks scale almost linearly with data volumes. Data mining is a major new challenge!  Jim Gray and Alex Szalay, The World Wide Telescope: An Archetype for Online Science, Comm. ACM, 45(11): 50-54, Nov. 2002
April 18, 2024 Data Mining: Concepts and Techniques 4 Evolution of Database Technology  1960s:  Data collection, database creation, IMS and network DBMS  1970s:  Relational data model, relational DBMS implementation  1980s:  RDBMS, advanced data models (extended-relational, OO, deductive, etc.)  Application-oriented DBMS (spatial, scientific, engineering, etc.)  1990s:  Data mining, data warehousing, multimedia databases, and Web databases  2000s  Stream data management and mining  Data mining and its applications  Web technology (XML, data integration) and global information systems
April 18, 2024 Data Mining: Concepts and Techniques 5 What Is Data Mining?  Data mining (knowledge discovery from data)  Extraction of interesting (non-trivial, implicit, previously unknown and potentially useful) patterns or knowledge from huge amount of data  Data mining: a misnomer?  Alternative names  Knowledge discovery (mining) in databases (KDD), knowledge extraction, data/pattern analysis, data archeology, data dredging, information harvesting, business intelligence, etc.  Watch out: Is everything “data mining”?  Simple search and query processing  (Deductive) expert systems
April 18, 2024 Data Mining: Concepts and Techniques 6 Knowledge Discovery (KDD) Process  Data mining—core of knowledge discovery process Data Cleaning Data Integration Databases Data Warehouse Task-relevant Data Selection Data Mining Pattern Evaluation
April 18, 2024 Data Mining: Concepts and Techniques 7 Data Mining and Business Intelligence Increasing potential to support business decisions End User Business Analyst Data Analyst DBA Decision Making Data Presentation Visualization Techniques Data Mining Information Discovery Data Exploration Statistical Summary, Querying, and Reporting Data Preprocessing/Integration, Data Warehouses Data Sources Paper, Files, Web documents, Scientific experiments, Database Systems
April 18, 2024 Data Mining: Concepts and Techniques 8 Data Mining: Confluence of Multiple Disciplines Data Mining Database Technology Statistics Machine Learning Pattern Recognition Algorithm Other Disciplines Visualization
April 18, 2024 Data Mining: Concepts and Techniques 9 Why Not Traditional Data Analysis?  Tremendous amount of data  Algorithms must be highly scalable to handle such as tera-bytes of data  High-dimensionality of data  Micro-array may have tens of thousands of dimensions  High complexity of data  Data streams and sensor data  Time-series data, temporal data, sequence data  Structure data, graphs, social networks and multi-linked data  Heterogeneous databases and legacy databases  Spatial, spatiotemporal, multimedia, text and Web data  Software programs, scientific simulations  New and sophisticated applications
April 18, 2024 Data Mining: Concepts and Techniques 10 Multi-Dimensional View of Data Mining  Data to be mined  Relational, data warehouse, transactional, stream, object- oriented/relational, active, spatial, time-series, text, multi-media, heterogeneous, legacy, WWW  Knowledge to be mined  Characterization, discrimination, association, classification, clustering, trend/deviation, outlier analysis, etc.  Multiple/integrated functions and mining at multiple levels  Techniques utilized  Database-oriented, data warehouse (OLAP), machine learning, statistics, visualization, etc.  Applications adapted  Retail, telecommunication, banking, fraud analysis, bio-data mining, stock market analysis, text mining, Web mining, etc.
April 18, 2024 Data Mining: Concepts and Techniques 11 Data Mining: Classification Schemes  General functionality  Descriptive data mining  Predictive data mining  Different views lead to different classifications  Data view: Kinds of data to be mined  Knowledge view: Kinds of knowledge to be discovered  Method view: Kinds of techniques utilized  Application view: Kinds of applications adapted
April 18, 2024 Data Mining: Concepts and Techniques 12 Data Mining: On What Kinds of Data?  Database-oriented data sets and applications  Relational database, data warehouse, transactional database  Advanced data sets and advanced applications  Data streams and sensor data  Time-series data, temporal data, sequence data (incl. bio-sequences)  Structure data, graphs, social networks and multi-linked data  Object-relational databases  Heterogeneous databases and legacy databases  Spatial data and spatiotemporal data  Multimedia database  Text databases  The World-Wide Web
April 18, 2024 Data Mining: Concepts and Techniques 13 Data Mining Functionalities  Multidimensional concept description: Characterization and discrimination  Generalize, summarize, and contrast data characteristics, e.g., dry vs. wet regions  Frequent patterns, association, correlation vs. causality  Diaper  Beer [0.5%, 75%] (Correlation or causality?)  Classification and prediction  Construct models (functions) that describe and distinguish classes or concepts for future prediction  E.g., classify countries based on (climate), or classify cars based on (gas mileage)  Predict some unknown or missing numerical values
April 18, 2024 Data Mining: Concepts and Techniques 14 Data Mining Functionalities (2)  Cluster analysis  Class label is unknown: Group data to form new classes, e.g., cluster houses to find distribution patterns  Maximizing intra-class similarity & minimizing interclass similarity  Outlier analysis  Outlier: Data object that does not comply with the general behavior of the data  Noise or exception? Useful in fraud detection, rare events analysis  Trend and evolution analysis  Trend and deviation: e.g., regression analysis  Sequential pattern mining: e.g., digital camera  large SD memory  Periodicity analysis  Similarity-based analysis  Other pattern-directed or statistical analyses
April 18, 2024 Data Mining: Concepts and Techniques 15 Top-10 Most Popular DM Algorithms: 18 Identified Candidates (I)  Classification  #1. C4.5: Quinlan, J. R. C4.5: Programs for Machine Learning. Morgan Kaufmann., 1993.  #2. CART: L. Breiman, J. Friedman, R. Olshen, and C. Stone. Classification and Regression Trees. Wadsworth, 1984.  #3. K Nearest Neighbours (kNN): Hastie, T. and Tibshirani, R. 1996. Discriminant Adaptive Nearest Neighbor Classification. TPAMI. 18(6)  #4. Naive Bayes Hand, D.J., Yu, K., 2001. Idiot's Bayes: Not So Stupid After All? Internat. Statist. Rev. 69, 385-398.  Statistical Learning  #5. SVM: Vapnik, V. N. 1995. The Nature of Statistical Learning Theory. Springer-Verlag.  #6. EM: McLachlan, G. and Peel, D. (2000). Finite Mixture Models. J. Wiley, New York. Association Analysis  #7. Apriori: Rakesh Agrawal and Ramakrishnan Srikant. Fast Algorithms for Mining Association Rules. In VLDB '94.  #8. FP-Tree: Han, J., Pei, J., and Yin, Y. 2000. Mining frequent patterns without candidate generation. In SIGMOD '00.
April 18, 2024 Data Mining: Concepts and Techniques 16 The 18 Identified Candidates (II)  Link Mining  #9. PageRank: Brin, S. and Page, L. 1998. The anatomy of a large-scale hypertextual Web search engine. In WWW-7, 1998.  #10. HITS: Kleinberg, J. M. 1998. Authoritative sources in a hyperlinked environment. SODA, 1998.  Clustering  #11. K-Means: MacQueen, J. B., Some methods for classification and analysis of multivariate observations, in Proc. 5th Berkeley Symp. Mathematical Statistics and Probability, 1967.  #12. BIRCH: Zhang, T., Ramakrishnan, R., and Livny, M. 1996. BIRCH: an efficient data clustering method for very large databases. In SIGMOD '96.  Bagging and Boosting  #13. AdaBoost: Freund, Y. and Schapire, R. E. 1997. A decision- theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55, 1 (Aug. 1997), 119-139.
April 18, 2024 Data Mining: Concepts and Techniques 17 The 18 Identified Candidates (III)  Sequential Patterns  #14. GSP: Srikant, R. and Agrawal, R. 1996. Mining Sequential Patterns: Generalizations and Performance Improvements. In Proceedings of the 5th International Conference on Extending Database Technology, 1996.  #15. PrefixSpan: J. Pei, J. Han, B. Mortazavi-Asl, H. Pinto, Q. Chen, U. Dayal and M-C. Hsu. PrefixSpan: Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth. In ICDE '01.  Integrated Mining  #16. CBA: Liu, B., Hsu, W. and Ma, Y. M. Integrating classification and association rule mining. KDD-98.  Rough Sets  #17. Finding reduct: Zdzislaw Pawlak, Rough Sets: Theoretical Aspects of Reasoning about Data, Kluwer Academic Publishers, Norwell, MA, 1992  Graph Mining  #18. gSpan: Yan, X. and Han, J. 2002. gSpan: Graph-Based Substructure Pattern Mining. In ICDM '02.
April 18, 2024 Data Mining: Concepts and Techniques 18 Top-10 Algorithm Finally Selected at ICDM’06  #1: C4.5 (61 votes)  #2: K-Means (60 votes)  #3: SVM (58 votes)  #4: Apriori (52 votes)  #5: EM (48 votes)  #6: PageRank (46 votes)  #7: AdaBoost (45 votes)  #7: kNN (45 votes)  #7: Naive Bayes (45 votes)  #10: CART (34 votes)
April 18, 2024 Data Mining: Concepts and Techniques 19 Major Issues in Data Mining  Mining methodology  Mining different kinds of knowledge from diverse data types, e.g., bio, stream, Web  Performance: efficiency, effectiveness, and scalability  Pattern evaluation: the interestingness problem  Incorporation of background knowledge  Handling noise and incomplete data  Parallel, distributed and incremental mining methods  Integration of the discovered knowledge with existing one: knowledge fusion  User interaction  Data mining query languages and ad-hoc mining  Expression and visualization of data mining results  Interactive mining of knowledge at multiple levels of abstraction  Applications and social impacts  Domain-specific data mining & invisible data mining  Protection of data security, integrity, and privacy
April 18, 2024 Data Mining: Concepts and Techniques 20 A Brief History of Data Mining Society  1989 IJCAI Workshop on Knowledge Discovery in Databases  Knowledge Discovery in Databases (G. Piatetsky-Shapiro and W. Frawley, 1991)  1991-1994 Workshops on Knowledge Discovery in Databases  Advances in Knowledge Discovery and Data Mining (U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, 1996)  1995-1998 International Conferences on Knowledge Discovery in Databases and Data Mining (KDD’95-98)  Journal of Data Mining and Knowledge Discovery (1997)  ACM SIGKDD conferences since 1998 and SIGKDD Explorations  More conferences on data mining  PAKDD (1997), PKDD (1997), SIAM-Data Mining (2001), (IEEE) ICDM (2001), etc.  ACM Transactions on KDD starting in 2007
April 18, 2024 Data Mining: Concepts and Techniques 21 Conferences and Journals on Data Mining  KDD Conferences  ACM SIGKDD Int. Conf. on Knowledge Discovery in Databases and Data Mining (KDD)  SIAM Data Mining Conf. (SDM)  (IEEE) Int. Conf. on Data Mining (ICDM)  Conf. on Principles and practices of Knowledge Discovery and Data Mining (PKDD)  Pacific-Asia Conf. on Knowledge Discovery and Data Mining (PAKDD)  Other related conferences  ACM SIGMOD  VLDB  (IEEE) ICDE  WWW, SIGIR  ICML, CVPR, NIPS  Journals  Data Mining and Knowledge Discovery (DAMI or DMKD)  IEEE Trans. On Knowledge and Data Eng. (TKDE)  KDD Explorations  ACM Trans. on KDD
April 18, 2024 Data Mining: Concepts and Techniques 22 Where to Find References? DBLP, CiteSeer, Google  Data mining and KDD (SIGKDD: CDROM)  Conferences: ACM-SIGKDD, IEEE-ICDM, SIAM-DM, PKDD, PAKDD, etc.  Journal: Data Mining and Knowledge Discovery, KDD Explorations, ACM TKDD  Database systems (SIGMOD: ACM SIGMOD Anthology—CD ROM)  Conferences: ACM-SIGMOD, ACM-PODS, VLDB, IEEE-ICDE, EDBT, ICDT, DASFAA  Journals: IEEE-TKDE, ACM-TODS/TOIS, JIIS, J. ACM, VLDB J., Info. Sys., etc.  AI & Machine Learning  Conferences: Machine learning (ML), AAAI, IJCAI, COLT (Learning Theory), CVPR, NIPS, etc.  Journals: Machine Learning, Artificial Intelligence, Knowledge and Information Systems, IEEE-PAMI, etc.  Web and IR  Conferences: SIGIR, WWW, CIKM, etc.  Journals: WWW: Internet and Web Information Systems,  Statistics  Conferences: Joint Stat. Meeting, etc.  Journals: Annals of statistics, etc.  Visualization  Conference proceedings: CHI, ACM-SIGGraph, etc.  Journals: IEEE Trans. visualization and computer graphics, etc.
April 18, 2024 Data Mining: Concepts and Techniques 23 Recommended Reference Books  S. Chakrabarti. Mining the Web: Statistical Analysis of Hypertex and Semi-Structured Data. Morgan Kaufmann, 2002  R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 2ed., Wiley-Interscience, 2000  T. Dasu and T. Johnson. Exploratory Data Mining and Data Cleaning. John Wiley & Sons, 2003  U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy. Advances in Knowledge Discovery and Data Mining. AAAI/MIT Press, 1996  U. Fayyad, G. Grinstein, and A. Wierse, Information Visualization in Data Mining and Knowledge Discovery, Morgan Kaufmann, 2001  J. Han and M. Kamber. Data Mining: Concepts and Techniques. Morgan Kaufmann, 2nd ed., 2006  D. J. Hand, H. Mannila, and P. Smyth, Principles of Data Mining, MIT Press, 2001  T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer-Verlag, 2001  B. Liu, Web Data Mining, Springer 2006.  T. M. Mitchell, Machine Learning, McGraw Hill, 1997  G. Piatetsky-Shapiro and W. J. Frawley. Knowledge Discovery in Databases. AAAI/MIT Press, 1991  P.-N. Tan, M. Steinbach and V. Kumar, Introduction to Data Mining, Wiley, 2005  S. M. Weiss and N. Indurkhya, Predictive Data Mining, Morgan Kaufmann, 1998  I. H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, Morgan Kaufmann, 2nd ed. 2005
April 18, 2024 Data Mining: Concepts and Techniques 24 Summary  Data mining: Discovering interesting patterns from large amounts of data  A natural evolution of database technology, in great demand, with wide applications  A KDD process includes data cleaning, data integration, data selection, transformation, data mining, pattern evaluation, and knowledge presentation  Mining can be performed in a variety of information repositories  Data mining functionalities: characterization, discrimination, association, classification, clustering, outlier and trend analysis, etc.  Data mining systems and architectures  Major issues in data mining
April 18, 2024 Data Mining: Concepts and Techniques 25
April 18, 2024 Data Mining: Concepts and Techniques 26 Supplementary Lecture Slides  Note: The slides following the end of chapter summary are supplementary slides that could be useful for supplementary readings or teaching  These slides may have its corresponding text contents in the book chapters, but were omitted due to limited time in author’s own course lecture  The slides in other chapters have similar convention and treatment
April 18, 2024 Data Mining: Concepts and Techniques 27 Why Data Mining?—Potential Applications  Data analysis and decision support  Market analysis and management  Target marketing, customer relationship management (CRM), market basket analysis, cross selling, market segmentation  Risk analysis and management  Forecasting, customer retention, improved underwriting, quality control, competitive analysis  Fraud detection and detection of unusual patterns (outliers)  Other Applications  Text mining (news group, email, documents) and Web mining  Stream data mining  Bioinformatics and bio-data analysis
April 18, 2024 Data Mining: Concepts and Techniques 28 Ex. 1: Market Analysis and Management  Where does the data come from?—Credit card transactions, loyalty cards, discount coupons, customer complaint calls, plus (public) lifestyle studies  Target marketing  Find clusters of “model” customers who share the same characteristics: interest, income level, spending habits, etc.  Determine customer purchasing patterns over time  Cross-market analysis—Find associations/co-relations between product sales, & predict based on such association  Customer profiling—What types of customers buy what products (clustering or classification)  Customer requirement analysis  Identify the best products for different groups of customers  Predict what factors will attract new customers  Provision of summary information  Multidimensional summary reports  Statistical summary information (data central tendency and variation)
April 18, 2024 Data Mining: Concepts and Techniques 29 Ex. 2: Corporate Analysis & Risk Management  Finance planning and asset evaluation  cash flow analysis and prediction  contingent claim analysis to evaluate assets  cross-sectional and time series analysis (financial-ratio, trend analysis, etc.)  Resource planning  summarize and compare the resources and spending  Competition  monitor competitors and market directions  group customers into classes and a class-based pricing procedure  set pricing strategy in a highly competitive market
April 18, 2024 Data Mining: Concepts and Techniques 30 Ex. 3: Fraud Detection & Mining Unusual Patterns  Approaches: Clustering & model construction for frauds, outlier analysis  Applications: Health care, retail, credit card service, telecomm.  Auto insurance: ring of collisions  Money laundering: suspicious monetary transactions  Medical insurance  Professional patients, ring of doctors, and ring of references  Unnecessary or correlated screening tests  Telecommunications: phone-call fraud  Phone call model: destination of the call, duration, time of day or week. Analyze patterns that deviate from an expected norm  Retail industry  Analysts estimate that 38% of retail shrink is due to dishonest employees  Anti-terrorism
April 18, 2024 Data Mining: Concepts and Techniques 31 KDD Process: Several Key Steps  Learning the application domain  relevant prior knowledge and goals of application  Creating a target data set: data selection  Data cleaning and preprocessing: (may take 60% of effort!)  Data reduction and transformation  Find useful features, dimensionality/variable reduction, invariant representation  Choosing functions of data mining  summarization, classification, regression, association, clustering  Choosing the mining algorithm(s)  Data mining: search for patterns of interest  Pattern evaluation and knowledge presentation  visualization, transformation, removing redundant patterns, etc.  Use of discovered knowledge
April 18, 2024 Data Mining: Concepts and Techniques 32 Are All the “Discovered” Patterns Interesting?  Data mining may generate thousands of patterns: Not all of them are interesting  Suggested approach: Human-centered, query-based, focused mining  Interestingness measures  A pattern is interesting if it is easily understood by humans, valid on new or test data with some degree of certainty, potentially useful, novel, or validates some hypothesis that a user seeks to confirm  Objective vs. subjective interestingness measures  Objective: based on statistics and structures of patterns, e.g., support, confidence, etc.  Subjective: based on user’s belief in the data, e.g., unexpectedness, novelty, actionability, etc.
April 18, 2024 Data Mining: Concepts and Techniques 33 Find All and Only Interesting Patterns?  Find all the interesting patterns: Completeness  Can a data mining system find all the interesting patterns? Do we need to find all of the interesting patterns?  Heuristic vs. exhaustive search  Association vs. classification vs. clustering  Search for only interesting patterns: An optimization problem  Can a data mining system find only the interesting patterns?  Approaches  First general all the patterns and then filter out the uninteresting ones  Generate only the interesting patterns—mining query optimization
April 18, 2024 Data Mining: Concepts and Techniques 34 Other Pattern Mining Issues  Precise patterns vs. approximate patterns  Association and correlation mining: possible find sets of precise patterns  But approximate patterns can be more compact and sufficient  How to find high quality approximate patterns??  Gene sequence mining: approximate patterns are inherent  How to derive efficient approximate pattern mining algorithms??  Constrained vs. non-constrained patterns  Why constraint-based mining?  What are the possible kinds of constraints? How to push constraints into the mining process?
April 18, 2024 Data Mining: Concepts and Techniques 35 A Few Announcements (Sept. 1)  A new section CS412ADD: CRN 48711 and its rules/arrangements  4th Unit for I2CS students  Survey report for mining new types of data  4th Unit for in-campus students  High quality implementation of one selected (to be discussed with TA/Instructor) data mining algorithm in the textbook  Or, a research report if you plan to devote your future research thesis on data mining
April 18, 2024 Data Mining: Concepts and Techniques 36 Why Data Mining Query Language?  Automated vs. query-driven?  Finding all the patterns autonomously in a database?—unrealistic because the patterns could be too many but uninteresting  Data mining should be an interactive process  User directs what to be mined  Users must be provided with a set of primitives to be used to communicate with the data mining system  Incorporating these primitives in a data mining query language  More flexible user interaction  Foundation for design of graphical user interface  Standardization of data mining industry and practice
April 18, 2024 Data Mining: Concepts and Techniques 37 Primitives that Define a Data Mining Task  Task-relevant data  Database or data warehouse name  Database tables or data warehouse cubes  Condition for data selection  Relevant attributes or dimensions  Data grouping criteria  Type of knowledge to be mined  Characterization, discrimination, association, classification, prediction, clustering, outlier analysis, other data mining tasks  Background knowledge  Pattern interestingness measurements  Visualization/presentation of discovered patterns
April 18, 2024 Data Mining: Concepts and Techniques 38 Primitive 3: Background Knowledge  A typical kind of background knowledge: Concept hierarchies  Schema hierarchy  E.g., street < city < province_or_state < country  Set-grouping hierarchy  E.g., {20-39} = young, {40-59} = middle_aged  Operation-derived hierarchy  email address: hagonzal@cs.uiuc.edu login-name < department < university < country  Rule-based hierarchy  low_profit_margin (X) <= price(X, P1) and cost (X, P2) and (P1 - P2) < $50
April 18, 2024 Data Mining: Concepts and Techniques 39 Primitive 4: Pattern Interestingness Measure  Simplicity e.g., (association) rule length, (decision) tree size  Certainty e.g., confidence, P(A|B) = #(A and B)/ #(B), classification reliability or accuracy, certainty factor, rule strength, rule quality, discriminating weight, etc.  Utility potential usefulness, e.g., support (association), noise threshold (description)  Novelty not previously known, surprising (used to remove redundant rules, e.g., Illinois vs. Champaign rule implication support ratio)
April 18, 2024 Data Mining: Concepts and Techniques 40 Primitive 5: Presentation of Discovered Patterns  Different backgrounds/usages may require different forms of representation  E.g., rules, tables, crosstabs, pie/bar chart, etc.  Concept hierarchy is also important  Discovered knowledge might be more understandable when represented at high level of abstraction  Interactive drill up/down, pivoting, slicing and dicing provide different perspectives to data  Different kinds of knowledge require different representation: association, classification, clustering, etc.
April 18, 2024 Data Mining: Concepts and Techniques 41 DMQL—A Data Mining Query Language  Motivation  A DMQL can provide the ability to support ad-hoc and interactive data mining  By providing a standardized language like SQL  Hope to achieve a similar effect like that SQL has on relational database  Foundation for system development and evolution  Facilitate information exchange, technology transfer, commercialization and wide acceptance  Design  DMQL is designed with the primitives described earlier
April 18, 2024 Data Mining: Concepts and Techniques 42 An Example Query in DMQL
April 18, 2024 Data Mining: Concepts and Techniques 43 Other Data Mining Languages & Standardization Efforts  Association rule language specifications  MSQL (Imielinski & Virmani’99)  MineRule (Meo Psaila and Ceri’96)  Query flocks based on Datalog syntax (Tsur et al’98)  OLEDB for DM (Microsoft’2000) and recently DMX (Microsoft SQLServer 2005)  Based on OLE, OLE DB, OLE DB for OLAP, C#  Integrating DBMS, data warehouse and data mining  DMML (Data Mining Mark-up Language) by DMG (www.dmg.org)  Providing a platform and process structure for effective data mining  Emphasizing on deploying data mining technology to solve business problems
April 18, 2024 Data Mining: Concepts and Techniques 44 Integration of Data Mining and Data Warehousing  Data mining systems, DBMS, Data warehouse systems coupling  No coupling, loose-coupling, semi-tight-coupling, tight-coupling  On-line analytical mining data  integration of mining and OLAP technologies  Interactive mining multi-level knowledge  Necessity of mining knowledge and patterns at different levels of abstraction by drilling/rolling, pivoting, slicing/dicing, etc.  Integration of multiple mining functions  Characterized classification, first clustering and then association
April 18, 2024 Data Mining: Concepts and Techniques 45 Coupling Data Mining with DB/DW Systems  No coupling—flat file processing, not recommended  Loose coupling  Fetching data from DB/DW  Semi-tight coupling—enhanced DM performance  Provide efficient implement a few data mining primitives in a DB/DW system, e.g., sorting, indexing, aggregation, histogram analysis, multiway join, precomputation of some stat functions  Tight coupling—A uniform information processing environment  DM is smoothly integrated into a DB/DW system, mining query is optimized based on mining query, indexing, query processing methods, etc.
April 18, 2024 Data Mining: Concepts and Techniques 46 Architecture: Typical Data Mining System data cleaning, integration, and selection Database or Data Warehouse Server Data Mining Engine Pattern Evaluation Graphical User Interface Knowl edge- Base Database Data Warehouse World-Wide Web Other Info Repositories

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Data Mining introduction and basic concepts

  • 1.
    April 18, 2024Data Mining: Concepts and Techniques 1 Chapter 1. Introduction  Motivation: Why data mining?  What is data mining?  Data Mining: On what kind of data?  Data mining functionality  Classification of data mining systems  Top-10 most popular data mining algorithms  Major issues in data mining  Overview of the course
  • 2.
    April 18, 2024Data Mining: Concepts and Techniques 2 Why Data Mining?  The Explosive Growth of Data: from terabytes to petabytes  Data collection and data availability  Automated data collection tools, database systems, Web, computerized society  Major sources of abundant data  Business: Web, e-commerce, transactions, stocks, …  Science: Remote sensing, bioinformatics, scientific simulation, …  Society and everyone: news, digital cameras, YouTube  We are drowning in data, but starving for knowledge!  “Necessity is the mother of invention”—Data mining—Automated analysis of massive data sets
  • 3.
    April 18, 2024Data Mining: Concepts and Techniques 3 Evolution of Sciences  Before 1600, empirical science  1600-1950s, theoretical science  Each discipline has grown a theoretical component. Theoretical models often motivate experiments and generalize our understanding.  1950s-1990s, computational science  Over the last 50 years, most disciplines have grown a third, computational branch (e.g. empirical, theoretical, and computational ecology, or physics, or linguistics.)  Computational Science traditionally meant simulation. It grew out of our inability to find closed-form solutions for complex mathematical models.  1990-now, data science  The flood of data from new scientific instruments and simulations  The ability to economically store and manage petabytes of data online  The Internet and computing Grid that makes all these archives universally accessible  Scientific info. management, acquisition, organization, query, and visualization tasks scale almost linearly with data volumes. Data mining is a major new challenge!  Jim Gray and Alex Szalay, The World Wide Telescope: An Archetype for Online Science, Comm. ACM, 45(11): 50-54, Nov. 2002
  • 4.
    April 18, 2024Data Mining: Concepts and Techniques 4 Evolution of Database Technology  1960s:  Data collection, database creation, IMS and network DBMS  1970s:  Relational data model, relational DBMS implementation  1980s:  RDBMS, advanced data models (extended-relational, OO, deductive, etc.)  Application-oriented DBMS (spatial, scientific, engineering, etc.)  1990s:  Data mining, data warehousing, multimedia databases, and Web databases  2000s  Stream data management and mining  Data mining and its applications  Web technology (XML, data integration) and global information systems
  • 5.
    April 18, 2024Data Mining: Concepts and Techniques 5 What Is Data Mining?  Data mining (knowledge discovery from data)  Extraction of interesting (non-trivial, implicit, previously unknown and potentially useful) patterns or knowledge from huge amount of data  Data mining: a misnomer?  Alternative names  Knowledge discovery (mining) in databases (KDD), knowledge extraction, data/pattern analysis, data archeology, data dredging, information harvesting, business intelligence, etc.  Watch out: Is everything “data mining”?  Simple search and query processing  (Deductive) expert systems
  • 6.
    April 18, 2024Data Mining: Concepts and Techniques 6 Knowledge Discovery (KDD) Process  Data mining—core of knowledge discovery process Data Cleaning Data Integration Databases Data Warehouse Task-relevant Data Selection Data Mining Pattern Evaluation
  • 7.
    April 18, 2024Data Mining: Concepts and Techniques 7 Data Mining and Business Intelligence Increasing potential to support business decisions End User Business Analyst Data Analyst DBA Decision Making Data Presentation Visualization Techniques Data Mining Information Discovery Data Exploration Statistical Summary, Querying, and Reporting Data Preprocessing/Integration, Data Warehouses Data Sources Paper, Files, Web documents, Scientific experiments, Database Systems
  • 8.
    April 18, 2024Data Mining: Concepts and Techniques 8 Data Mining: Confluence of Multiple Disciplines Data Mining Database Technology Statistics Machine Learning Pattern Recognition Algorithm Other Disciplines Visualization
  • 9.
    April 18, 2024Data Mining: Concepts and Techniques 9 Why Not Traditional Data Analysis?  Tremendous amount of data  Algorithms must be highly scalable to handle such as tera-bytes of data  High-dimensionality of data  Micro-array may have tens of thousands of dimensions  High complexity of data  Data streams and sensor data  Time-series data, temporal data, sequence data  Structure data, graphs, social networks and multi-linked data  Heterogeneous databases and legacy databases  Spatial, spatiotemporal, multimedia, text and Web data  Software programs, scientific simulations  New and sophisticated applications
  • 10.
    April 18, 2024Data Mining: Concepts and Techniques 10 Multi-Dimensional View of Data Mining  Data to be mined  Relational, data warehouse, transactional, stream, object- oriented/relational, active, spatial, time-series, text, multi-media, heterogeneous, legacy, WWW  Knowledge to be mined  Characterization, discrimination, association, classification, clustering, trend/deviation, outlier analysis, etc.  Multiple/integrated functions and mining at multiple levels  Techniques utilized  Database-oriented, data warehouse (OLAP), machine learning, statistics, visualization, etc.  Applications adapted  Retail, telecommunication, banking, fraud analysis, bio-data mining, stock market analysis, text mining, Web mining, etc.
  • 11.
    April 18, 2024Data Mining: Concepts and Techniques 11 Data Mining: Classification Schemes  General functionality  Descriptive data mining  Predictive data mining  Different views lead to different classifications  Data view: Kinds of data to be mined  Knowledge view: Kinds of knowledge to be discovered  Method view: Kinds of techniques utilized  Application view: Kinds of applications adapted
  • 12.
    April 18, 2024Data Mining: Concepts and Techniques 12 Data Mining: On What Kinds of Data?  Database-oriented data sets and applications  Relational database, data warehouse, transactional database  Advanced data sets and advanced applications  Data streams and sensor data  Time-series data, temporal data, sequence data (incl. bio-sequences)  Structure data, graphs, social networks and multi-linked data  Object-relational databases  Heterogeneous databases and legacy databases  Spatial data and spatiotemporal data  Multimedia database  Text databases  The World-Wide Web
  • 13.
    April 18, 2024Data Mining: Concepts and Techniques 13 Data Mining Functionalities  Multidimensional concept description: Characterization and discrimination  Generalize, summarize, and contrast data characteristics, e.g., dry vs. wet regions  Frequent patterns, association, correlation vs. causality  Diaper  Beer [0.5%, 75%] (Correlation or causality?)  Classification and prediction  Construct models (functions) that describe and distinguish classes or concepts for future prediction  E.g., classify countries based on (climate), or classify cars based on (gas mileage)  Predict some unknown or missing numerical values
  • 14.
    April 18, 2024Data Mining: Concepts and Techniques 14 Data Mining Functionalities (2)  Cluster analysis  Class label is unknown: Group data to form new classes, e.g., cluster houses to find distribution patterns  Maximizing intra-class similarity & minimizing interclass similarity  Outlier analysis  Outlier: Data object that does not comply with the general behavior of the data  Noise or exception? Useful in fraud detection, rare events analysis  Trend and evolution analysis  Trend and deviation: e.g., regression analysis  Sequential pattern mining: e.g., digital camera  large SD memory  Periodicity analysis  Similarity-based analysis  Other pattern-directed or statistical analyses
  • 15.
    April 18, 2024Data Mining: Concepts and Techniques 15 Top-10 Most Popular DM Algorithms: 18 Identified Candidates (I)  Classification  #1. C4.5: Quinlan, J. R. C4.5: Programs for Machine Learning. Morgan Kaufmann., 1993.  #2. CART: L. Breiman, J. Friedman, R. Olshen, and C. Stone. Classification and Regression Trees. Wadsworth, 1984.  #3. K Nearest Neighbours (kNN): Hastie, T. and Tibshirani, R. 1996. Discriminant Adaptive Nearest Neighbor Classification. TPAMI. 18(6)  #4. Naive Bayes Hand, D.J., Yu, K., 2001. Idiot's Bayes: Not So Stupid After All? Internat. Statist. Rev. 69, 385-398.  Statistical Learning  #5. SVM: Vapnik, V. N. 1995. The Nature of Statistical Learning Theory. Springer-Verlag.  #6. EM: McLachlan, G. and Peel, D. (2000). Finite Mixture Models. J. Wiley, New York. Association Analysis  #7. Apriori: Rakesh Agrawal and Ramakrishnan Srikant. Fast Algorithms for Mining Association Rules. In VLDB '94.  #8. FP-Tree: Han, J., Pei, J., and Yin, Y. 2000. Mining frequent patterns without candidate generation. In SIGMOD '00.
  • 16.
    April 18, 2024Data Mining: Concepts and Techniques 16 The 18 Identified Candidates (II)  Link Mining  #9. PageRank: Brin, S. and Page, L. 1998. The anatomy of a large-scale hypertextual Web search engine. In WWW-7, 1998.  #10. HITS: Kleinberg, J. M. 1998. Authoritative sources in a hyperlinked environment. SODA, 1998.  Clustering  #11. K-Means: MacQueen, J. B., Some methods for classification and analysis of multivariate observations, in Proc. 5th Berkeley Symp. Mathematical Statistics and Probability, 1967.  #12. BIRCH: Zhang, T., Ramakrishnan, R., and Livny, M. 1996. BIRCH: an efficient data clustering method for very large databases. In SIGMOD '96.  Bagging and Boosting  #13. AdaBoost: Freund, Y. and Schapire, R. E. 1997. A decision- theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55, 1 (Aug. 1997), 119-139.
  • 17.
    April 18, 2024Data Mining: Concepts and Techniques 17 The 18 Identified Candidates (III)  Sequential Patterns  #14. GSP: Srikant, R. and Agrawal, R. 1996. Mining Sequential Patterns: Generalizations and Performance Improvements. In Proceedings of the 5th International Conference on Extending Database Technology, 1996.  #15. PrefixSpan: J. Pei, J. Han, B. Mortazavi-Asl, H. Pinto, Q. Chen, U. Dayal and M-C. Hsu. PrefixSpan: Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth. In ICDE '01.  Integrated Mining  #16. CBA: Liu, B., Hsu, W. and Ma, Y. M. Integrating classification and association rule mining. KDD-98.  Rough Sets  #17. Finding reduct: Zdzislaw Pawlak, Rough Sets: Theoretical Aspects of Reasoning about Data, Kluwer Academic Publishers, Norwell, MA, 1992  Graph Mining  #18. gSpan: Yan, X. and Han, J. 2002. gSpan: Graph-Based Substructure Pattern Mining. In ICDM '02.
  • 18.
    April 18, 2024Data Mining: Concepts and Techniques 18 Top-10 Algorithm Finally Selected at ICDM’06  #1: C4.5 (61 votes)  #2: K-Means (60 votes)  #3: SVM (58 votes)  #4: Apriori (52 votes)  #5: EM (48 votes)  #6: PageRank (46 votes)  #7: AdaBoost (45 votes)  #7: kNN (45 votes)  #7: Naive Bayes (45 votes)  #10: CART (34 votes)
  • 19.
    April 18, 2024Data Mining: Concepts and Techniques 19 Major Issues in Data Mining  Mining methodology  Mining different kinds of knowledge from diverse data types, e.g., bio, stream, Web  Performance: efficiency, effectiveness, and scalability  Pattern evaluation: the interestingness problem  Incorporation of background knowledge  Handling noise and incomplete data  Parallel, distributed and incremental mining methods  Integration of the discovered knowledge with existing one: knowledge fusion  User interaction  Data mining query languages and ad-hoc mining  Expression and visualization of data mining results  Interactive mining of knowledge at multiple levels of abstraction  Applications and social impacts  Domain-specific data mining & invisible data mining  Protection of data security, integrity, and privacy
  • 20.
    April 18, 2024Data Mining: Concepts and Techniques 20 A Brief History of Data Mining Society  1989 IJCAI Workshop on Knowledge Discovery in Databases  Knowledge Discovery in Databases (G. Piatetsky-Shapiro and W. Frawley, 1991)  1991-1994 Workshops on Knowledge Discovery in Databases  Advances in Knowledge Discovery and Data Mining (U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, 1996)  1995-1998 International Conferences on Knowledge Discovery in Databases and Data Mining (KDD’95-98)  Journal of Data Mining and Knowledge Discovery (1997)  ACM SIGKDD conferences since 1998 and SIGKDD Explorations  More conferences on data mining  PAKDD (1997), PKDD (1997), SIAM-Data Mining (2001), (IEEE) ICDM (2001), etc.  ACM Transactions on KDD starting in 2007
  • 21.
    April 18, 2024Data Mining: Concepts and Techniques 21 Conferences and Journals on Data Mining  KDD Conferences  ACM SIGKDD Int. Conf. on Knowledge Discovery in Databases and Data Mining (KDD)  SIAM Data Mining Conf. (SDM)  (IEEE) Int. Conf. on Data Mining (ICDM)  Conf. on Principles and practices of Knowledge Discovery and Data Mining (PKDD)  Pacific-Asia Conf. on Knowledge Discovery and Data Mining (PAKDD)  Other related conferences  ACM SIGMOD  VLDB  (IEEE) ICDE  WWW, SIGIR  ICML, CVPR, NIPS  Journals  Data Mining and Knowledge Discovery (DAMI or DMKD)  IEEE Trans. On Knowledge and Data Eng. (TKDE)  KDD Explorations  ACM Trans. on KDD
  • 22.
    April 18, 2024Data Mining: Concepts and Techniques 22 Where to Find References? DBLP, CiteSeer, Google  Data mining and KDD (SIGKDD: CDROM)  Conferences: ACM-SIGKDD, IEEE-ICDM, SIAM-DM, PKDD, PAKDD, etc.  Journal: Data Mining and Knowledge Discovery, KDD Explorations, ACM TKDD  Database systems (SIGMOD: ACM SIGMOD Anthology—CD ROM)  Conferences: ACM-SIGMOD, ACM-PODS, VLDB, IEEE-ICDE, EDBT, ICDT, DASFAA  Journals: IEEE-TKDE, ACM-TODS/TOIS, JIIS, J. ACM, VLDB J., Info. Sys., etc.  AI & Machine Learning  Conferences: Machine learning (ML), AAAI, IJCAI, COLT (Learning Theory), CVPR, NIPS, etc.  Journals: Machine Learning, Artificial Intelligence, Knowledge and Information Systems, IEEE-PAMI, etc.  Web and IR  Conferences: SIGIR, WWW, CIKM, etc.  Journals: WWW: Internet and Web Information Systems,  Statistics  Conferences: Joint Stat. Meeting, etc.  Journals: Annals of statistics, etc.  Visualization  Conference proceedings: CHI, ACM-SIGGraph, etc.  Journals: IEEE Trans. visualization and computer graphics, etc.
  • 23.
    April 18, 2024Data Mining: Concepts and Techniques 23 Recommended Reference Books  S. Chakrabarti. Mining the Web: Statistical Analysis of Hypertex and Semi-Structured Data. Morgan Kaufmann, 2002  R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 2ed., Wiley-Interscience, 2000  T. Dasu and T. Johnson. Exploratory Data Mining and Data Cleaning. John Wiley & Sons, 2003  U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy. Advances in Knowledge Discovery and Data Mining. AAAI/MIT Press, 1996  U. Fayyad, G. Grinstein, and A. Wierse, Information Visualization in Data Mining and Knowledge Discovery, Morgan Kaufmann, 2001  J. Han and M. Kamber. Data Mining: Concepts and Techniques. Morgan Kaufmann, 2nd ed., 2006  D. J. Hand, H. Mannila, and P. Smyth, Principles of Data Mining, MIT Press, 2001  T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer-Verlag, 2001  B. Liu, Web Data Mining, Springer 2006.  T. M. Mitchell, Machine Learning, McGraw Hill, 1997  G. Piatetsky-Shapiro and W. J. Frawley. Knowledge Discovery in Databases. AAAI/MIT Press, 1991  P.-N. Tan, M. Steinbach and V. Kumar, Introduction to Data Mining, Wiley, 2005  S. M. Weiss and N. Indurkhya, Predictive Data Mining, Morgan Kaufmann, 1998  I. H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, Morgan Kaufmann, 2nd ed. 2005
  • 24.
    April 18, 2024Data Mining: Concepts and Techniques 24 Summary  Data mining: Discovering interesting patterns from large amounts of data  A natural evolution of database technology, in great demand, with wide applications  A KDD process includes data cleaning, data integration, data selection, transformation, data mining, pattern evaluation, and knowledge presentation  Mining can be performed in a variety of information repositories  Data mining functionalities: characterization, discrimination, association, classification, clustering, outlier and trend analysis, etc.  Data mining systems and architectures  Major issues in data mining
  • 25.
    April 18, 2024Data Mining: Concepts and Techniques 25
  • 26.
    April 18, 2024Data Mining: Concepts and Techniques 26 Supplementary Lecture Slides  Note: The slides following the end of chapter summary are supplementary slides that could be useful for supplementary readings or teaching  These slides may have its corresponding text contents in the book chapters, but were omitted due to limited time in author’s own course lecture  The slides in other chapters have similar convention and treatment
  • 27.
    April 18, 2024Data Mining: Concepts and Techniques 27 Why Data Mining?—Potential Applications  Data analysis and decision support  Market analysis and management  Target marketing, customer relationship management (CRM), market basket analysis, cross selling, market segmentation  Risk analysis and management  Forecasting, customer retention, improved underwriting, quality control, competitive analysis  Fraud detection and detection of unusual patterns (outliers)  Other Applications  Text mining (news group, email, documents) and Web mining  Stream data mining  Bioinformatics and bio-data analysis
  • 28.
    April 18, 2024Data Mining: Concepts and Techniques 28 Ex. 1: Market Analysis and Management  Where does the data come from?—Credit card transactions, loyalty cards, discount coupons, customer complaint calls, plus (public) lifestyle studies  Target marketing  Find clusters of “model” customers who share the same characteristics: interest, income level, spending habits, etc.  Determine customer purchasing patterns over time  Cross-market analysis—Find associations/co-relations between product sales, & predict based on such association  Customer profiling—What types of customers buy what products (clustering or classification)  Customer requirement analysis  Identify the best products for different groups of customers  Predict what factors will attract new customers  Provision of summary information  Multidimensional summary reports  Statistical summary information (data central tendency and variation)
  • 29.
    April 18, 2024Data Mining: Concepts and Techniques 29 Ex. 2: Corporate Analysis & Risk Management  Finance planning and asset evaluation  cash flow analysis and prediction  contingent claim analysis to evaluate assets  cross-sectional and time series analysis (financial-ratio, trend analysis, etc.)  Resource planning  summarize and compare the resources and spending  Competition  monitor competitors and market directions  group customers into classes and a class-based pricing procedure  set pricing strategy in a highly competitive market
  • 30.
    April 18, 2024Data Mining: Concepts and Techniques 30 Ex. 3: Fraud Detection & Mining Unusual Patterns  Approaches: Clustering & model construction for frauds, outlier analysis  Applications: Health care, retail, credit card service, telecomm.  Auto insurance: ring of collisions  Money laundering: suspicious monetary transactions  Medical insurance  Professional patients, ring of doctors, and ring of references  Unnecessary or correlated screening tests  Telecommunications: phone-call fraud  Phone call model: destination of the call, duration, time of day or week. Analyze patterns that deviate from an expected norm  Retail industry  Analysts estimate that 38% of retail shrink is due to dishonest employees  Anti-terrorism
  • 31.
    April 18, 2024Data Mining: Concepts and Techniques 31 KDD Process: Several Key Steps  Learning the application domain  relevant prior knowledge and goals of application  Creating a target data set: data selection  Data cleaning and preprocessing: (may take 60% of effort!)  Data reduction and transformation  Find useful features, dimensionality/variable reduction, invariant representation  Choosing functions of data mining  summarization, classification, regression, association, clustering  Choosing the mining algorithm(s)  Data mining: search for patterns of interest  Pattern evaluation and knowledge presentation  visualization, transformation, removing redundant patterns, etc.  Use of discovered knowledge
  • 32.
    April 18, 2024Data Mining: Concepts and Techniques 32 Are All the “Discovered” Patterns Interesting?  Data mining may generate thousands of patterns: Not all of them are interesting  Suggested approach: Human-centered, query-based, focused mining  Interestingness measures  A pattern is interesting if it is easily understood by humans, valid on new or test data with some degree of certainty, potentially useful, novel, or validates some hypothesis that a user seeks to confirm  Objective vs. subjective interestingness measures  Objective: based on statistics and structures of patterns, e.g., support, confidence, etc.  Subjective: based on user’s belief in the data, e.g., unexpectedness, novelty, actionability, etc.
  • 33.
    April 18, 2024Data Mining: Concepts and Techniques 33 Find All and Only Interesting Patterns?  Find all the interesting patterns: Completeness  Can a data mining system find all the interesting patterns? Do we need to find all of the interesting patterns?  Heuristic vs. exhaustive search  Association vs. classification vs. clustering  Search for only interesting patterns: An optimization problem  Can a data mining system find only the interesting patterns?  Approaches  First general all the patterns and then filter out the uninteresting ones  Generate only the interesting patterns—mining query optimization
  • 34.
    April 18, 2024Data Mining: Concepts and Techniques 34 Other Pattern Mining Issues  Precise patterns vs. approximate patterns  Association and correlation mining: possible find sets of precise patterns  But approximate patterns can be more compact and sufficient  How to find high quality approximate patterns??  Gene sequence mining: approximate patterns are inherent  How to derive efficient approximate pattern mining algorithms??  Constrained vs. non-constrained patterns  Why constraint-based mining?  What are the possible kinds of constraints? How to push constraints into the mining process?
  • 35.
    April 18, 2024Data Mining: Concepts and Techniques 35 A Few Announcements (Sept. 1)  A new section CS412ADD: CRN 48711 and its rules/arrangements  4th Unit for I2CS students  Survey report for mining new types of data  4th Unit for in-campus students  High quality implementation of one selected (to be discussed with TA/Instructor) data mining algorithm in the textbook  Or, a research report if you plan to devote your future research thesis on data mining
  • 36.
    April 18, 2024Data Mining: Concepts and Techniques 36 Why Data Mining Query Language?  Automated vs. query-driven?  Finding all the patterns autonomously in a database?—unrealistic because the patterns could be too many but uninteresting  Data mining should be an interactive process  User directs what to be mined  Users must be provided with a set of primitives to be used to communicate with the data mining system  Incorporating these primitives in a data mining query language  More flexible user interaction  Foundation for design of graphical user interface  Standardization of data mining industry and practice
  • 37.
    April 18, 2024Data Mining: Concepts and Techniques 37 Primitives that Define a Data Mining Task  Task-relevant data  Database or data warehouse name  Database tables or data warehouse cubes  Condition for data selection  Relevant attributes or dimensions  Data grouping criteria  Type of knowledge to be mined  Characterization, discrimination, association, classification, prediction, clustering, outlier analysis, other data mining tasks  Background knowledge  Pattern interestingness measurements  Visualization/presentation of discovered patterns
  • 38.
    April 18, 2024Data Mining: Concepts and Techniques 38 Primitive 3: Background Knowledge  A typical kind of background knowledge: Concept hierarchies  Schema hierarchy  E.g., street < city < province_or_state < country  Set-grouping hierarchy  E.g., {20-39} = young, {40-59} = middle_aged  Operation-derived hierarchy  email address: hagonzal@cs.uiuc.edu login-name < department < university < country  Rule-based hierarchy  low_profit_margin (X) <= price(X, P1) and cost (X, P2) and (P1 - P2) < $50
  • 39.
    April 18, 2024Data Mining: Concepts and Techniques 39 Primitive 4: Pattern Interestingness Measure  Simplicity e.g., (association) rule length, (decision) tree size  Certainty e.g., confidence, P(A|B) = #(A and B)/ #(B), classification reliability or accuracy, certainty factor, rule strength, rule quality, discriminating weight, etc.  Utility potential usefulness, e.g., support (association), noise threshold (description)  Novelty not previously known, surprising (used to remove redundant rules, e.g., Illinois vs. Champaign rule implication support ratio)
  • 40.
    April 18, 2024Data Mining: Concepts and Techniques 40 Primitive 5: Presentation of Discovered Patterns  Different backgrounds/usages may require different forms of representation  E.g., rules, tables, crosstabs, pie/bar chart, etc.  Concept hierarchy is also important  Discovered knowledge might be more understandable when represented at high level of abstraction  Interactive drill up/down, pivoting, slicing and dicing provide different perspectives to data  Different kinds of knowledge require different representation: association, classification, clustering, etc.
  • 41.
    April 18, 2024Data Mining: Concepts and Techniques 41 DMQL—A Data Mining Query Language  Motivation  A DMQL can provide the ability to support ad-hoc and interactive data mining  By providing a standardized language like SQL  Hope to achieve a similar effect like that SQL has on relational database  Foundation for system development and evolution  Facilitate information exchange, technology transfer, commercialization and wide acceptance  Design  DMQL is designed with the primitives described earlier
  • 42.
    April 18, 2024Data Mining: Concepts and Techniques 42 An Example Query in DMQL
  • 43.
    April 18, 2024Data Mining: Concepts and Techniques 43 Other Data Mining Languages & Standardization Efforts  Association rule language specifications  MSQL (Imielinski & Virmani’99)  MineRule (Meo Psaila and Ceri’96)  Query flocks based on Datalog syntax (Tsur et al’98)  OLEDB for DM (Microsoft’2000) and recently DMX (Microsoft SQLServer 2005)  Based on OLE, OLE DB, OLE DB for OLAP, C#  Integrating DBMS, data warehouse and data mining  DMML (Data Mining Mark-up Language) by DMG (www.dmg.org)  Providing a platform and process structure for effective data mining  Emphasizing on deploying data mining technology to solve business problems
  • 44.
    April 18, 2024Data Mining: Concepts and Techniques 44 Integration of Data Mining and Data Warehousing  Data mining systems, DBMS, Data warehouse systems coupling  No coupling, loose-coupling, semi-tight-coupling, tight-coupling  On-line analytical mining data  integration of mining and OLAP technologies  Interactive mining multi-level knowledge  Necessity of mining knowledge and patterns at different levels of abstraction by drilling/rolling, pivoting, slicing/dicing, etc.  Integration of multiple mining functions  Characterized classification, first clustering and then association
  • 45.
    April 18, 2024Data Mining: Concepts and Techniques 45 Coupling Data Mining with DB/DW Systems  No coupling—flat file processing, not recommended  Loose coupling  Fetching data from DB/DW  Semi-tight coupling—enhanced DM performance  Provide efficient implement a few data mining primitives in a DB/DW system, e.g., sorting, indexing, aggregation, histogram analysis, multiway join, precomputation of some stat functions  Tight coupling—A uniform information processing environment  DM is smoothly integrated into a DB/DW system, mining query is optimized based on mining query, indexing, query processing methods, etc.
  • 46.
    April 18, 2024Data Mining: Concepts and Techniques 46 Architecture: Typical Data Mining System data cleaning, integration, and selection Database or Data Warehouse Server Data Mining Engine Pattern Evaluation Graphical User Interface Knowl edge- Base Database Data Warehouse World-Wide Web Other Info Repositories